%0 Conference Paper %1 citeulike:14391860 %A Hosseini, Roya %A Brusilovsky, Peter %A Yudelson, Michael %A Hellas, Arto %B Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2017 %I ACM %K MOOC assessment clustering data-mining myown performance-prediction programming stereotype stereotype-modeling student-modeling %P 76--84 %R 10.1145/3079628.3079672 %T Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses %U http://dx.doi.org/10.1145/3079628.3079672 %X Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task -- predicting student problem-solving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research. %@ 978-1-4503-4635-1

@inproceedings{citeulike:14391860, abstract = {Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in {MOOC} context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task -- predicting student problem-solving and learning in two programming courses and two {MOOCs}. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research.}, added-at = {2017-07-12T14:38:48.000+0200}, address = {New York, NY, USA}, author = {Hosseini, Roya and Brusilovsky, Peter and Yudelson, Michael and Hellas, Arto}, biburl = {https://www.bibsonomy.org/bibtex/2904bda28d6afb3981d7eaa20d8f5701a/brusilovsky}, booktitle = {Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization}, citeulike-article-id = {14391860}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3079672}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/3079628.3079672}, description = {CiteULike: Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses}, doi = {10.1145/3079628.3079672}, interhash = {7e15497fede925881dd3f7e6be5af1b4}, intrahash = {904bda28d6afb3981d7eaa20d8f5701a}, isbn = {978-1-4503-4635-1}, keywords = {MOOC assessment clustering data-mining myown performance-prediction programming stereotype stereotype-modeling student-modeling}, location = {Bratislava, Slovakia}, pages = {76--84}, posted-at = {2017-07-12 13:35:38}, priority = {0}, publisher = {ACM}, series = {UMAP '17}, timestamp = {2017-07-12T14:38:48.000+0200}, title = {Stereotype Modeling for {Problem-Solving} Performance Predictions in {MOOCs} and Traditional Courses}, url = {http://dx.doi.org/10.1145/3079628.3079672}, year = 2017 }